Method of distributed aggregation in a call center

ABSTRACT

A method for partitioning a call center having N agents associated with M agent groups, for computation by a plurality of computational nodes, has steps for (a) assigning each agent as a vertex in a hypergraph; (b) assigning each agent group as a hyper-edge in the hypergraph; and (c) applying a hypergraph partitioning algorithm to partition the agents and groups relative to the nodes with the hypergraph cost function awarding equal load distribution to nodes and minimizing inter-node traffic.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/606,792, filed on Sep. 7, 2012, the disclosure of which is herebyincorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention is in the technical area of call centers, and pertainsmore particularly to creating statistics for a call center.

2. Description of Related Art

Call centers are typically organizations of telephone switches,computerized servers and agent stations inter-related by networks. Thetelephone switches are typically connected to Publically SwitchedTelephone Networks (PSTN), cellular telephone networks, and the Internet(for data-packet telephony). In a local call center most stations andcomputerized resources are inter-connected by a local area network(LAN), but there may also be more remote hardware and softwareresources, and agents as well interfacing by a wide area network, whichmay be the well-known Internet network.

Call center technology is a rapidly-growing field, and has been so forsome time, so modern call centers can be very large organizations,encompassing hundreds or even thousands of agents manning agentstations, and handling tens of thousands of calls.

In operation of call centers it is vitally important to have at leastnear real-time information concerning the state of resources, such asagent activity and availability, calls in queue, and much more. Thisinformation is used to plan resource adjustment, pace operations and toenable many other functions.

In most call centers there is therefore an operation termed Stat-Serverin the art, which is a term used to refer to a computerized serverappliance, or a group of such appliances, network connected to the agentstations and many other servers inter-related in the call center. TheStat Server executes powerful software from a non-transitory physicalmedium, and the software provides functionality for accepting data frommany other points in the call center, monitors many call centerfunctions and status, and performs highly-structured computerizedoperations to maintain and report statistics and status of a widevariety of call center functions in near real time. These status reportsand statistics are vital in operation of any call center, which wouldquickly fail without such functions.

It was described above that call centers have evolved to a point that asingle call center may have thousands of agents and may handle tens ofthousands of calls. Moreover, there are federations of call centers,interfaced, for example, over the Internet, and the trend is to everlarger and larger operations.

In earlier days of call center technology, where a call center mighthave, for example twenty agent stations, manned by agents to handleperhaps a few hundred calls per day, a Stat Server having a singleprocessing unit (node) and moderate computer power, was adequate toprovide the information needed to operate the call center successfully.With the present state of call center technology, parallel processing(multiple nodes) and quite sophisticated software is more and morenecessary to provide the real-time information and data needed forsuccessful operation.

With multiple node parallel processing, and the complicated structure ofcall centers and their complicated functionality, partitioning a callcenter for parallel processing becomes a necessity, and brings newproblems, one of which is inter-node data traffic, which severely limitsthe real-time desire of Stat Server operations.

What is clearly needed are methods for partitioning that limitinter-node traffic and provide real time operation with minimumcomputing power.

BRIEF SUMMARY OF THE INVENTION

In an embodiment of the present invention a method for partitioning acall center having N agents associated with M agent groups, forcomputation by a plurality of computational nodes is provided, havingsteps for (a) assigning each agent as a vertex in a hypergraph; (b)assigning each agent group as a hyper-edge in the; and (c) applying ahypergraph partitioning algorithm to partition the agents and groupsrelative to the nodes with the hypergraph cost function awarding equalload distribution to nodes and minimizing inter-node traffic.

In another embodiment there are further steps for (d) mapping each agentand group as an object O to one of K nodes by a Hash function H:O→[1,K],such that each node H(O) is an owner for the object; (e) for each agent,computing the aggregates AA_1, . . . , AA_P locally by the associatedowner node; and (f) computing aggregates AA_1, . . . , AA_P for eachgroup shared by all K nodes by i) creating a partial state record (PSR)of the aggregates AG_j by Node i by aggregating on all agents, owned bynode i and belonging to group G; and ii) sending PSRs to owner (G),which combines all PSRs into a total state record.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is an exemplary architectural diagram of a network-connectedsystem that includes a call center in an embodiment of the presentinvention.

FIG. 2 is a simplified diagram illustrating connectivity of Nodes andData sources in an embodiment of the present invention.

FIG. 3 is represents a simplified case of three agents, two agent groupsand two nodes in an embodiment of the invention.

FIG. 4 is a process flow chart illustrating steps in practice of theinvention in one embodiment.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is an exemplary architectural diagram of a network-connectedsystem that includes a call center 111 in an embodiment of the presentinvention. It is to be noted that this architecture is an example, andthere are many perturbations that might be made. It is also to be notedthat there may be several more call centers sharing traffic anddistributing calls to agents, all under the purview of a single StatServer 126 operation. This exemplary system comprises awide-area-network (WAN) 102, a public-switched telephone network (PSTN)101, and a wireless carrier network (WN) 103. PSTN 101 may be anypublicly switched telephone network. WAN 102 may be a corporate orpublic WAN, and may be the well-known Internet network. Wireless network103 may be any wireless carrier network and is typically a cellulartelephony network.

WAN 102 is the Internet network in a preferred embodiment because of itshigh public access characteristic, and is referred to herein as Internet102. Internet 102 is further exemplified by a network backbone 104representing all of the lines, equipment, and connection points thatmake up the Internet as a whole. Therefore, there are no geographiclimitations to the practice of the present invention.

Network backbone 104 in this example supports a web server 109, whichmay be hosted by any one of a wide variety of enterprises ororganizations, and represents all web servers that may be instantiatedin the Internet.

A call center 111 is illustrated in this example, built upon a localarea network (LAN) 118 supporting various equipment and facilities forpracticing call-center interaction processing. LAN 118 supports aplurality of call center agents dedicated to services for the host ofthe call center. Each call center agent in this example operates from anagent station 116 (1-n). Each agent station 116 (1-n) includes aLAN-connected computing appliance and a switch-connected telephone forillustrative purposes only, as the exact equipment types and operationmay vary. The telephone capability at agent stations may be providedthrough the LAN as digital telephony, as shown in this example, or thetelephones may be connected by Destination Number lines (not shown) to aPSTN switch connected, as is switch 131, to PSTN 101 by trunk 117.

PSTN 101 includes a network-level telephone switch 105, which may be anautomated call distributor (ACD) or a private branch exchange (PBX), orsome other type of telephony switching facility without departing fromthe spirit and scope of the present invention. Telephone switch 105 isconnected to central office telephone switch 131 associated with thecall center via telephony trunk 117. Switch 131 represents the last hopfor callers before being routed to agent stations 116 (1-n) viatelephone. Switch 105 in PSTN 101 has connection to network backbone 104of the Internet network through a telephone gateway 108. Gateway 108 isadapted by software executing from a physical medium to facilitate crossconversion of telephony traffic from the PSTN to the Internet networkand from the Internet over the PSTN network.

A variety of consumer appliances 115 (1-4) are illustrated in thisexample and are meant to include any appliances that may be used toaccess networks 102, 101, and 103. Appliance 115 (1) is a desktopcomputing appliance with a digital telephone and SW executing to enablethe telephone. In an alternative embodiment the telephone may be aseparate PSTN telephone connected by PSTN land-line to PSTN network 101.

A consumer operating equipment 115 (1) connects with computer 115 (1) toInternet 102 via PSTN land line 107, and an Internet service provider(ISP) 106, in this instance through gateway 108. The methods ofconnection may vary upon the equipment used and the available technicalavenues for accessing the Internet. Cable modem, telephone modem,satellite, digital services line (DSL), broadband, and WiFi are justsome of the available connection methods that may be used to gain accessto Internet 102.

Consumer appliances 115 (2), 115 (3) and 115 (4) are wirelessly enabledto connect to network backbone 104 via a cell tower 112, a transceiver113, and a wireless multimedia gateway (WGW) 114 for bridgingcommunications between wireless network 103 and Internet 102. Consumerappliance 115 (2) is a Laptop computer and 115 (3) is a cellulartelephone, such as an iPhone or an Android telephone. Computingappliance 115 (4) is an iPad type device. It may be assumed in thisexample, that each of the users operating appliances 115 (1-4) mayinitiate and manage telephone calls, multi-media transactions, emails,and web-browsing sessions.

LAN 118 in call center 111 supports a routing server 122 connected toInternet backbone 104 by way of an Internet access line. Routing server122 includes a physical digital medium 124 that stores all of the dataand software required to enable interaction routing. All transactionrequests made by users from appliances 115 (1-4) are sent to routingserver 122 for distribution to agents operating at agent stations 116(1-n), managed by routing software that may employ many intelligentrouting functions. Telephone switch 131 is enhanced for intelligentrouting of voice interactions via a computer telephony integration (CTI)processor 120. CTI processor 120 is connected to switch 131 via a CTIlink. CTI processor 120 provides intelligent control over switch 131.Telephone switch 131 also has an interactive voice response (IVR)capability via an IVR 119.

LAN 118 supports an application server 121 that may be employed forserving voice applications to callers. CTI processor 120 is connected toLAN 118 enabling service to the switch and other maintenance through CTIfunctionality. LAN 118 supports a messaging server 125 adapted with aphysical digital medium containing all of the required software and datato enable function as a message server or router. LAN 118 also supportsa statistics server (Stat Server) 126. Stat Server 126 includes aphysical non-transitory digital medium comprising all of the softwareand data required to enable function as a Stat Server. The software ofStat Server 126 is represented as SW 128. Stat server 126 has connectionto a data repository 127 adapted to store all call center statistics,including, for example, profiles regarding customers and clients of callcenter 111, and may also store profiles and statistics regarding agentsassociated with the call center. Stat Server 126, repository 127 andoperating SW 128 is a central focus in an embodiment of the presentinvention.

FIG. 2 is a simplified diagram illustrating connectivity of Nodes andData sources in an embodiment of the present invention. Node 1 and Node2 are shown connected by a data link 205, which can be any one of anumber of known data paths. Nodes 1 and 2 communicate over this path asneeded, but a goal of the present invention is to minimize inter-nodetraffic in this path. In this example Node 1 connects to a Data Source 1(203) and Node 2 connects to a Data Source 2 (204), which in turnconnect to a data network shown as LAN 118 in this example, but whichmight be a wide-area network such as the Internet, or another network.Data from the represented is operated upon by processing nodes N1 and N2in embodiments of the present invention, wherein Node 1 executes SW 128a and Node 2 executes SW 128 b. These two software instances are a partof SW 128 indicated in FIG. 1, and may be similar or quite different.

In embodiments of the present invention the call center is partitioned,and objects, such as agents and agent groups, are associated byconfiguration with one or more nodes. So computations using incomingdata to produce statistics regarding one set of agents, for example, maybe accomplished by Node 1, and computations regarding another set ofagents may be performed by Node 2. Depending upon the sophistication ofconfiguration there may be a great deal of inter-node traffic. Ifconfiguration is good there may be moderate inter-node traffic, and ifconfiguration is excellent, there may be very little inter-node traffic.Inter-node traffic is in general undesirable, because it requires timeand computing power to accomplish.

In an embodiment of the present invention a simplified model of thecontact center is treated, which consists of N agents and Magent-groups. This is simplified because it treats only of agents andagent groups, and the method of the invention is applicable in otherembodiments to other objects in the contact center.

It is well known that in a modern call center agents are typicallygrouped into functional groups. One group, for example, may be dedicatedto responding to connections made in outbound campaigns, while anothermay be dedicated to incoming calls regarding technical support for aparticular class of products. Incoming callers may be screened by IVR119(FIG. 1) and calls routed to agents in particular groups as agentsbecome available according to the callers purpose or need. It is usualfor an agent to be a part of more than one group, so the relationshipbetween agents and groups is many-to-many.

FIG. 3 represents a simplified case of three agents (A1, A2 and A3), twoagent groups (G1 and G2) and two nodes N1 and N2. The relationshipbetween agents nodes is typically physical, so all three agents may beassociated with N1. In that case N2, processing stats for G2, mustcollect information regarding all three agents from N1, and inter-nodetraffic will be considerable.

The skilled artisan will understand that in this architecture there maybe hundreds or even thousands of agents, dozens of groups, and aplurality of nodes. The goal is to partition the call center in such away to minimize inter-node traffic and provide real-time stats.

In operation there is a set of aggregates AA_1, . . . , AA_P to becomputed for each agent, and a set of aggregates AG_1, . . . , AG_S tobe computed for each agent-group. It is assumed that at least allagent-group aggregates will be distributive or algebraic, so that itwill be possible to efficiently compute the aggregates in thedistributed system. More information on this aspect is referenced from[1] J. Han and M. Kamber, “Data Mining: Concepts and Techniques”, 2nded., Morgan Kaufmann Publishers, March 2006.

If N and M are above a certain threshold it is not computationallyfeasible to calculate the aggregates in the single node. This is wherecall center technology has evolved, so it is necessary to partition thecontact center. It is desired to spread all agents between Kcommunicating nodes of the distributed system, so that each nodeperforms computations at least approximately the same rate, which willminimize inter-node traffic.

In an embodiment of the present invention the contact center is modeledas a hyper-graph. Hypergraphs are defined generally in Wikipedia athttp://en.wikipedia.org/wiki/Hypergraph. In an embodiment of theinvention each agent is considered a vertex with the unit weight (ormore general weight W=f(complexity(AA_1), . . . , complexity(AA_P))). Anagent-group (Group_i) is considered the hyper-edge (AGENT_i_1, . . . ,AGENT_i_L).

In terms of hyper-graphs, the problem is the problem of hyper-graphpartitioning, described fully in “Multilevel hypergraph partitioning:applications in VLSI domain”, by Karypis et. al, March 1999, IEEETransactions on Very Large Scale Integration (VLSI) Systems 7 (1), pp.69-79, with cost function awarding equal load distribution andminimizing the inter-node traffic, which can be efficiently solved usingexisting software packages as may be derived from [4] Zoltan Toolkit,available athttp://www.cs.sandia.gov/zoltan/dev_html/dev_intro_sqe.html.

The four references mentioned above are listed again here:

-   [1] J. Han and M. Kamber, “Data Mining: Concepts and Techniques”,    2nd ed., Morgan Kaufmann Publishers, March 2006-   [2] Hyper-graph, http://en.wikipedia.org/wiki/Hypergraph-   [3] “Multilevel hypergraph partitioning: applications in VLSI    domain”, by Karypis et. al, March 1999, IEEE Transactions on Very    Large Scale Integration (VLSI) Systems 7 (1), pp. 69-79-   [4] Zoltan Toolkit,    http://www.cs.sandia.gov/zoltan/dev_html/dev_intro_sqe.html

All four references are incorporated in the instant specification atleast by reference.

After the Contact Center hyper-graph is partitioned, the aggregation isperformed as follows.

-   -   Each object (agent or group) is mapped to a node using hash        function

H:O→[1,K]

We call node H(O) an owner for the object O. Each node can easilydetermine the owner for any object, using this completely decentralizedlogic. For each agent, the aggregates AA_1, . . . , AA_P are computed instraight-forward manner locally by owner node. The computation ofaggregates AG_1, . . . , AG_S for each group Group_i is, in general,shared by all K nodes as follows:

Node i maintains partial state record (PSR) of the aggregate AG _j byaggregating on all agents, owned by node i and belonging to group G.Then, PSR is sent to owner(G), which combines all PSRs into total staterecord (which is possible because of the aggregates beingdistributive/algebraic). The traffic minimization embedded into costfunction encourages placing all agents of a given group into the samenode (partition), so in the ideal case there is almost no inter-nodetraffic (one node computes the single PSR and sends it to owner node).

FIG. 4 is a process flow chart depicting steps in the procedure in anembodiment of the invention for partitioning a call center using ahypergraph technique. A starting point is illustrated as element 401. Atstep 402 each agent is assigned as a vertex in a hypergraph with aweight W=f (complexity (AA_1), . . . , complexity (AA_P)). At step 403each agent group is assigned as a hyperedge in the hypergraph(Agent_i_1, . . . , Agent_i_L).

At step 404 a software function known for the hypergraph is applied topartition the agents and groups relative to the nodes with thehypergraph cost function awarding equal load distribution to nodes andminimizing inter-node traffic. At step 405 each agent and group ismapped as an object O to one of K nodes by Hash function H:O→[1,K], suchthat each node is an owner for the object. At step 406 aggregates AA_1,. . . , AA_P are computed for each agent locally by the owner node.Finally, at step 407 aggregates AA_1, . . . , AA_P are computed for eachgroup with the computation being shared by all nodes. Each node icreates a partial state record of the aggregates AG _j by aggregatingall agents owned by node I and belonging to group G, and sending PSRs toowner (G), which combines all PSRs into a total state record.

It will be apparent to the skilled artisan that the embodimentsdescribed are exemplary, and intentionally generalized to avoid unduecomplication in description. It will also be apparent to the skilledperson that many alterations might be made in the embodiments describedwithout departing from the invention, and that would be covered by theclaims and enabled by the specification and drawings.

1. A method for partitioning a call center having N agents associatedwith M agent groups, for computation by a plurality of computationalnodes, comprising the steps of: (a) assigning each agent as a vertex ina hypergraph; (b) assigning each agent group as a hyper-edge in the; and(c) applying a hypergraph partitioning algorithm to partition the agentsand groups relative to the nodes with the hypergraph cost functionawarding equal load distribution to nodes and minimizing inter-nodetraffic.
 2. The method of claim 1 further comprising aggregation steps:(d) mapping each agent and group as an object O to one of K nodes by aHash function H:O→[1,K], such that each node H(O) is an owner for theobject; (e) for each agent, computing the aggregates AA_1, . . . , AA_Plocally by the associated owner node; and (f) computing aggregates AA_1,. . . , AA_P for each group shared by all K nodes: i) creating a partialstate record (PSR) of the aggregates AG_j by Node i by aggregating onall agents, owned by node i and belonging to group G; and ii) sendingPSRs to owner (G), which combines all PSRs into a total state record.